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132 lines
5.4 KiB
132 lines
5.4 KiB
import os
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from pathlib import Path
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from typing import Any, Callable, Dict, List, Optional, Tuple, Union
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from torch import Tensor
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from .folder import find_classes, make_dataset
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from .video_utils import VideoClips
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from .vision import VisionDataset
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class UCF101(VisionDataset):
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"""
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`UCF101 <https://www.crcv.ucf.edu/data/UCF101.php>`_ dataset.
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UCF101 is an action recognition video dataset.
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This dataset consider every video as a collection of video clips of fixed size, specified
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by ``frames_per_clip``, where the step in frames between each clip is given by
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``step_between_clips``. The dataset itself can be downloaded from the dataset website;
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annotations that ``annotation_path`` should be pointing to can be downloaded from `here
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<https://www.crcv.ucf.edu/data/UCF101/UCF101TrainTestSplits-RecognitionTask.zip>`_.
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To give an example, for 2 videos with 10 and 15 frames respectively, if ``frames_per_clip=5``
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and ``step_between_clips=5``, the dataset size will be (2 + 3) = 5, where the first two
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elements will come from video 1, and the next three elements from video 2.
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Note that we drop clips which do not have exactly ``frames_per_clip`` elements, so not all
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frames in a video might be present.
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Internally, it uses a VideoClips object to handle clip creation.
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Args:
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root (str or ``pathlib.Path``): Root directory of the UCF101 Dataset.
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annotation_path (str): path to the folder containing the split files;
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see docstring above for download instructions of these files
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frames_per_clip (int): number of frames in a clip.
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step_between_clips (int, optional): number of frames between each clip.
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fold (int, optional): which fold to use. Should be between 1 and 3.
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train (bool, optional): if ``True``, creates a dataset from the train split,
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otherwise from the ``test`` split.
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transform (callable, optional): A function/transform that takes in a TxHxWxC video
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and returns a transformed version.
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output_format (str, optional): The format of the output video tensors (before transforms).
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Can be either "THWC" (default) or "TCHW".
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Returns:
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tuple: A 3-tuple with the following entries:
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- video (Tensor[T, H, W, C] or Tensor[T, C, H, W]): The `T` video frames
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- audio(Tensor[K, L]): the audio frames, where `K` is the number of channels
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and `L` is the number of points
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- label (int): class of the video clip
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"""
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def __init__(
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self,
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root: Union[str, Path],
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annotation_path: str,
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frames_per_clip: int,
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step_between_clips: int = 1,
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frame_rate: Optional[int] = None,
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fold: int = 1,
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train: bool = True,
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transform: Optional[Callable] = None,
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_precomputed_metadata: Optional[Dict[str, Any]] = None,
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num_workers: int = 1,
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_video_width: int = 0,
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_video_height: int = 0,
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_video_min_dimension: int = 0,
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_audio_samples: int = 0,
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output_format: str = "THWC",
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) -> None:
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super().__init__(root)
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if not 1 <= fold <= 3:
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raise ValueError(f"fold should be between 1 and 3, got {fold}")
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extensions = ("avi",)
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self.fold = fold
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self.train = train
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self.classes, class_to_idx = find_classes(self.root)
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self.samples = make_dataset(self.root, class_to_idx, extensions, is_valid_file=None)
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video_list = [x[0] for x in self.samples]
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video_clips = VideoClips(
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video_list,
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frames_per_clip,
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step_between_clips,
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frame_rate,
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_precomputed_metadata,
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num_workers=num_workers,
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_video_width=_video_width,
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_video_height=_video_height,
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_video_min_dimension=_video_min_dimension,
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_audio_samples=_audio_samples,
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output_format=output_format,
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)
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# we bookkeep the full version of video clips because we want to be able
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# to return the metadata of full version rather than the subset version of
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# video clips
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self.full_video_clips = video_clips
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self.indices = self._select_fold(video_list, annotation_path, fold, train)
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self.video_clips = video_clips.subset(self.indices)
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self.transform = transform
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@property
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def metadata(self) -> Dict[str, Any]:
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return self.full_video_clips.metadata
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def _select_fold(self, video_list: List[str], annotation_path: str, fold: int, train: bool) -> List[int]:
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name = "train" if train else "test"
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name = f"{name}list{fold:02d}.txt"
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f = os.path.join(annotation_path, name)
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selected_files = set()
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with open(f) as fid:
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data = fid.readlines()
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data = [x.strip().split(" ")[0] for x in data]
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data = [os.path.join(self.root, *x.split("/")) for x in data]
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selected_files.update(data)
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indices = [i for i in range(len(video_list)) if video_list[i] in selected_files]
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return indices
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def __len__(self) -> int:
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return self.video_clips.num_clips()
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def __getitem__(self, idx: int) -> Tuple[Tensor, Tensor, int]:
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video, audio, info, video_idx = self.video_clips.get_clip(idx)
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label = self.samples[self.indices[video_idx]][1]
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if self.transform is not None:
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video = self.transform(video)
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return video, audio, label
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